Business Intelligence based novel Marketing Strategy Approach using Automatic Speech Recognition and Text Summarization

A novel marketing strategy approach is proposed that offers distinctive features than other competitive models. Text converted from speech between customer and sales executive is fed into text summarizer to automate customer strategy into various priorities. The approach shows that there is no need of data entry for emulating marketing tools. The need of account for customer's preferences is high, which can be stored by capturing the customer's context. Data mining and advanced analytical aspects give accurate results to help marketing managers and improve firms with solid customer strategies. The experimental results for the implementation show high outcomes in comparison to native advances in the technologies. A total of 62 test cases have been performed where an average of 12% Word Error Rate has been observed for Speech Recognition, which is optimal than other standard Speech Datasets. Furthermore, summarization compressed rate has been reported as 60%. The automated categorization module has achieved 70 % accuracy and overall F-score is 0.81349.